1. Area of the Art
The present invention relates to planning models, and more particularly, to a method for optimizing a planning model while managing strategic objectives.
2. Description of the Prior Art
As information technology continues to penetrate into all aspects of the economy, a wealth of data describing each of the millions of transactions that occur every minute is being collected and stored in on-line transaction processing (OLTP) databases, data warehouses, and other data repositories. This information, combined with quantitative research into the behavior of the value chain, allows analysts to develop enterprise models, which can predict how important quantities such as cost, sales, and gross margin will change when certain decisions, corresponding to inputs of the model, are made. These models go beyond simple rules-based approaches, such as those embodied in expert systems, and have the capability of generating a whole range of decisions that would not otherwise be obvious to a designer of rules-based systems.
There is however a problem with the use of model-based decision-making tools. As the decision-making process is automated, the operational decisions that are recommended by the model may begin to deviate from broader considerations that are not specifically built into the enterprise planning model. The reason for this is that an economic model can realistically succeed only on either a small scale or large scale, but not on both. Incorporating both small scale decisions and large scale decisions into a single enterprise planning model would result in a model of enormous complexity, making the optimization of the enterprise planning model computationally impractical, and economically inefficient.
The importance of this problem can be illustrated with an example from the retail industry. A retailer can use a demand model to accurately forecast each item's unit sales given the item's price and other factors. However, if the demand model is used directly to optimize pricing decisions, it will generate prices that vary greatly from those of a human pricing manager. This is because a demand model has no knowledge of the enterprise's strategic objectives, and therefore generates prices that do not reflect the company's overall strategic pricing policy. That is, a business enterprise does not blindly set prices with an aim towards a maximum number of sales. Rather, each business follows an overall strategy that it hopes will allow it to succeed economically in the long run.
One example of business strategy and strategic goals is the “price image” of a retailer. Some retailers position themselves as “high end” operations. They stock more expensive goods and often a more extensive range of goods. Often the retail surroundings are expensive in appearance and a large amount of individual attention is given to each customer. The customer naturally expects prices to be higher at a “high end” retailer. On the opposite end of the scale is the low cost or discount retailer. Here the prices are lower-often much lower—but the stock selection may be limited, the surroundings are spartan and individual customer service may be nonexistent. Well known economic models tell us that the lower prices of the discount retailer will result in sales of more units. Yet this does not necessarily translate into increased profit.
In maintaining a “high end” image, the retailer eschews the volume sales that discount pricing may afford. The higher prices stemming from a high price image operation may yield considerable profit. A careful cultivation of a particular price image can readily result in economic success both for the high end and the low end retailer. In the sense of economic ecology, such pricing strategies place the retailers in different niches so that they do not directly compete with each other. Maximizing the number of niches increases the possible number of retailers and maximizes the amount of money they can extract from the economy. This is but one example of a strategic decision or goal. An ordinary enterprise model cannot combine optimization of enterprise decisions with the retailer's strategic goals; consequently, the model's utility is greatly impacted. This inability to align and optimize an enterprise's operational decisions with its strategic objectives is a huge problem, and results in billion-dollar pricing inefficiencies in the retailing industry alone.